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The evolution of autonomous driving has made remarkable advancements inrecent years, evolving into a tangible reality. However, a human-centriclarge-scale adoption hinges on meeting a variety of multifaceted requirements.To ensure that the autonomous system meets the user's intent, it is essentialto accurately discern and interpret user commands, especially in complex oremergency situations. To this end, we propose to leverage the reasoningcapabilities of Large Language Models (LLMs) to infer system requirements fromin-cabin users' commands. Through a series of experiments that includedifferent LLM models and prompt designs, we explore the few-shot multivariatebinary classification accuracy of system requirements from natural languagetextual commands. We confirm the general ability of LLMs to understand andreason about prompts but underline that their effectiveness is conditioned onthe quality of both the LLM model and the design of appropriate sequentialprompts. Code and models are public with the link\url{https://github.com/KTH-RPL/DriveCmd_LLM}.

Human-Centric Autonomous Systems With LLMs for User Command Reasoning